Ecological Dynamics: Integrating Empirical, Statistical, and Analytical Methods

Laubmeier, Amanda N.; Cazelles, Bernard; Cuddington, Kim; Erickson, Kelley D.; Fortin, Marie‐Josée; Ogle, Kiona; Wikle, Christopher K.; Zhu, Kai; Zipkin, Elise F.

doi:10.1016/j.tree.2020.08.006

Cited by 16 publications

(9 citation statements)

References 31 publications

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“…Depend on the types of model identifiability, there are various examples and techniques to address the issues of model identifiability [ 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 ]. Here, we offer our perspective on this issue.…”

Section: Materials and Methodsmentioning

confidence: 99%

Practical Understanding of Cancer Model Identifiability in Clinical Applications

Phan

Bennett

Patten

2023

Life

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Mathematical models are a core component in the foundation of cancer theory and have been developed as clinical tools in precision medicine. Modeling studies for clinical applications often assume an individual’s characteristics can be represented as parameters in a model and are used to explain, predict, and optimize treatment outcomes. However, this approach relies on the identifiability of the underlying mathematical models. In this study, we build on the framework of an observing-system simulation experiment to study the identifiability of several models of cancer growth, focusing on the prognostic parameters of each model. Our results demonstrate that the frequency of data collection, the types of data, such as cancer proxy, and the accuracy of measurements all play crucial roles in determining the identifiability of the model. We also found that highly accurate data can allow for reasonably accurate estimates of some parameters, which may be the key to achieving model identifiability in practice. As more complex models required more data for identification, our results support the idea of using models with a clear mechanism that tracks disease progression in clinical settings. For such a model, the subset of model parameters associated with disease progression naturally minimizes the required data for model identifiability.

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Section: Materials and Methodsmentioning

confidence: 99%

Practical Understanding of Cancer Model Identifiability in Clinical Applications

Phan

Bennett

Patten

2023

Life

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“…Integrating theoretical and empirical approaches is often heralded as an ideal path for progress in ecology and evolutionary biology (Jeltsch et al, 2013 ; Laubmeier et al, 2020 ; Servedio, 2020 ; Shou et al, 2015 ), but such integration remains relatively limited (Scheiner, 2013 ). One likely barrier is that students are often not exposed to extensive quantitative training in traditional biology curricula (Chiel et al, 2010 ), and as a result, theoretical models remain intimidating for many empirical researchers (Grainger et al, 2022 ; Haller, 2014 ).…”

Section: Discussionmentioning

confidence: 99%

EcoEvoApps: Interactive apps for theoretical models in ecology and evolutionary biology

McGuire

Hayashi

Yan

et al. 2022

Ecology and Evolution

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The integration of theory and data drives progress in science, but a persistent barrier to such integration in ecology and evolutionary biology is that theory is often developed and expressed in the form of mathematical models that can feel daunting and inaccessible for students and empiricists with variable quantitative training and attitudes towards math. A promising way to make mathematical models more approachable is to embed them into interactive tools with which one can visually evaluate model structures and directly explore model outcomes through simulation. To promote such interactive learning of quantitative models, we developed EcoEvoApps, a collection of free, open‐source, and multilingual R/Shiny apps that include model overviews, interactive model simulations, and code to implement these models directly in R. The package currently focuses on canonical models of population dynamics, species interactions, and landscape ecology. These apps help illustrate fundamental results from theoretical ecology and can serve as valuable teaching tools in classroom settings. We present data from student surveys which show that students rate these apps as useful learning tools, and that using interactive apps leads to substantial gains in students' interest and confidence in working with mathematical models. This points to the potential for interactive activities to make theoretical models more accessible to a wider audience, and thus facilitate the feedback between theory and data across ecology and evolutionary biology.

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“…Integrating theoretical and empirical approaches is often heralded as an ideal path for progress in ecology and evolutionary biology (Jeltsch et al 2013, Shou et al 2015, Laubmeier et al 2020, Servedio 2020), but such integration remains relatively limited (Scheiner 2013). One likely barrier is that students are often not exposed to extensive quantitative training in traditional biology curricula (Chiel et al 2010), and as a result, theoretical models remain intimidating for many empirical researchers (Haller 2014, Grainger et al 2022).…”

Section: Discussionmentioning

confidence: 99%

EcoEvoApps: Interactive Apps for Theoretical Models in Ecology and Evolutionary Biology

McGuire

Hayashi

Yan

et al. 2021

Preprint

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Theoretical models are fundamental in Ecology and Evolutionary Biology (EEB), but they can be difficult to teach in undergraduate classrooms because students come in with inconsistent mathematical training and varying attitudes towards mathematics. A promising way to make these models more approachable is to use active learning exercises that allow model exploration and simulation. Thus, we present EcoEvoApps, a collection of free, open-source interactive web apps that simulate various theoretical EEB models. By surveying students who used these apps in two remote undergraduate Ecology courses in the U.S.A., we found that using the apps led to considerable gains in students' confidence and understanding of the focal models and underlying concepts. We envision EcoEvoApps as a widely available, equitable tool for students around the globe. Consequently, we have developed EcoEvoApps as a fundamentally collaborative platform, with an aim to build an international community of EEB researchers, educators, and students.

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Ecological Dynamics: Integrating Empirical, Statistical, and Analytical Methods

Cited by 16 publications

References 31 publications

Practical Understanding of Cancer Model Identifiability in Clinical Applications

Practical Understanding of Cancer Model Identifiability in Clinical Applications

EcoEvoApps: Interactive apps for theoretical models in ecology and evolutionary biology

EcoEvoApps: Interactive Apps for Theoretical Models in Ecology and Evolutionary Biology

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